Gini Methodology (eBook)

Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers.

This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology.

Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model.  With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.

Shlomo Yitzhaki received his B.A. in Economics and Statistics from The Hebrew University, and his M.A. in Economics Cum Laude and Ph.D. from The Hebrew University. He is currently Government Statistician at the Central Bureau of Statistics, Israel and Professor Emeritus, Dept. of Economics, at the Hebrew University, Jerusalem.  Shlomo Yitzhaki was the recipient of the annual prize of the Israeli Data Processing  Association in 1974 for the construction of a Tax Model. Besides significant public appointments with the Israeli government, he was a consutant at the World Bank and held visiting scholar positions at Harvard University, Falk Institute, and the Hoover Institution.  Shlomo Yitzhaki has served on the board of many prominent economic journals including: Economics Bulletin, National Tax Journal, The Journal of Economic Inequality, Review of Income and Wealth, and European Journal of Political Economy.

Edna Schechtman received a B.Sc. in Mathematics and Statistics, Hebrew University of Jerusalem (1971); M.A. in Statistics, Hebrew university (1976); Ph.D. in Statistics, Ohio State University (1980). She is a professor of Statistics at Ben Gurion University, Israel. Her main research interests are in the field of measures based on the Gini index as well as in applied Statistics in various areas such as medicine, road safety, quality control and more. She published over 100 papers in the professional literature. Professor Schechtman was the president o

f the Israeli Statistical Association. She recently spent 6 months at Stern business school at NYU and one semester at the department of Statistics at Berkeley as a visiting scholar and is a frequent visitor of the department of Statistics at Texas A&M university.

 

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Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers.This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model. With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.